Quality of Life Research

, Volume 5, Issue 1, pp 20–26 | Cite as

Establishing equivalence between scaled measures of quality of life

  • R. Gonin
  • S. Lloyd
  • D. Cella
Research Papers

Abstract

In this paper, methodologies which have been used in the pharmaceutical industry to demonstrate the equivalence of drug preparations, are applied to the measurement of quality of life (QOL). This approach is feasible when the generated data are measured on the same scale. Data from the quality of life instruments are transformed into interval scales by means of an appropriate scaling procedure. It is demonstrated that equivalence of QOL instruments is linked by a linear relationship between the QOL instruments Functional Assessment of Cancer Therapy (FACT) and the Functional Living Index-Cancer (FLIC). The linear relationship is derived using orthogonal least squares regression which takes into account that both measures are subject to error.

Key words

Bias bio-equivalence bootstrap standard errors Functional Assessment of Cancer Therapy Functional Living Index-Cancer orthogonal least squares quality of life 

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Aaronson NK, Ahmedzai S, Bergman B, et al. The European Organization for Research and Treatment of Cancer QLQ- C30: A quality of life instrument for use in international clinical trials in oncology. J Nat Cancer Inst 1993; 85(5): 365–376.Google Scholar
  2. 2.
    Schipper H, Clinch J, McMurray A, Levitt M. Measuring the quality of life of cancer patients: The Functional Living Index-Cancer: Development and validation. J Clin Oncol 1984; 2: 472–483.Google Scholar
  3. 3.
    Cella DF, Tulsky DS, Gray G, et al. The functional assessment of cancer therapy (FACT) Scale: Development and validation of the general version. J Clin Oncol 1993; 3: 570–579.Google Scholar
  4. 4.
    Schag CAC, Ganz P, Heinrich RL. Cancer Rehabilitation Evaluation System—Short Form: A cancer specific rehabilitation and quality of life instrument. Cancer 1991; 68: 1406–1413.Google Scholar
  5. 5.
    Spitzer WO, Dobson AJ, Hall J, et al. Measuring the quality of life of cancer patients: A concise QOL-Index for use by physicians. J Chronic Dis 1981; 34: 585–597.Google Scholar
  6. 6.
    Zubrod CG, Schneiderman M, Frei E, et al. Appraisal of methods for the study of chemotherapy of cancer in man: comparative therapeutic trial of nitrogen mustard and triethylene thiophosphoramide. J Chronic Dis 1960; 11: 7–33.Google Scholar
  7. 7.
    Karnofsky DA, Burchenal JH. Clinical evaluation of chemotherapeutic agents in cancer. In: Macleod CM, ed. Evaluation of chemotherapeutic agents. New York: Columbia University Press, 1949: 191–205.Google Scholar
  8. 8.
    Fishman B, Pasternak S, Wallenstein MS, Houde RW, Holland JC, Foley KM. The Memorial Pain Assessment Card: A valid instrument for the evaluation of cancer pain. Cancer 1987; 60: 1151–1158.Google Scholar
  9. 9.
    Daut RL, Cleeland CS, Flanery RC. Development of the Wisconsin brief pain questionnaire to assess pain in cancer and other diseases. Pain 1983; 17: 197–210.Google Scholar
  10. 10.
    Andrich D. Scaling attitude items constructed and scored in the Likert tradition. Educational and Psychological Measurement 1978; 38: 665–680.Google Scholar
  11. 11.
    Andrich D. A model for contingency tables having an ordered response classification. Biometrics 1979; 35: 403–415.Google Scholar
  12. 12.
    Wright BD and Linacre JM. A user's guide to BIGSTEPS Rasch model computer program version 2.1. Chicago: Mesa Press, 1991.Google Scholar
  13. 13.
    Wright BD and Stone MH. Best Test Design. Chicago: Mesa Press, 1979.Google Scholar
  14. 14.
    Wright BD and Masters GN. Rating scale analysis: Rasch measurement. Chicago: Mesa Press, 1982.Google Scholar
  15. 15.
    Spitzer RL, Cohen J, Fleiss JL and Endicott J. Quantification of agreement in psychiatric diagnosis. Arch Gen Psychiatry 1967; 17: 83–87.Google Scholar
  16. 16.
    Altman DG, Bland JM. Measurement in Medicine: The analysis of method comparison studies. Statistician 1983; 32: 307–317.Google Scholar
  17. 17.
    Hauck WW, Anderson S. A proposal for interpreting and reporting negative studies. Statis in Med 1986; 5: 203–209.Google Scholar
  18. 18.
    Westlake WJ. Biovailability and bioequivalence of pharmaceutical formulations. In: Peace KE, ed. Biopharmaceutical Statistics for Drug Development. New York: Marcel Dekker, 1988: 329–352.Google Scholar
  19. 19.
    Jackson JD and Dunlevy JA. Orthogonal least squares and the interchangeability of alternative proxy variables in the social sciences. Statistician 1988; 37: 7–14.Google Scholar
  20. 20.
    Anderson PO. Large sample and jackknife procedures for small sample orthogonal least squares inference. Communications in Stat 1975; 4: 193–202.Google Scholar
  21. 21.
    Feldmann U, Schneider B, Klinkers H. A multivariate approach for the biometric comparison of analytic methods in clinical chemistry. J Clin Chem Clin Biochem 1981; 19: 121–137.Google Scholar
  22. 22.
    Efron B. The jackknife the bootstrap and other resampling plans. Philadelphia: SIAM Press, 1982: 27–36.Google Scholar

Copyright information

© Rapid Science Publishers 1996

Authors and Affiliations

  • R. Gonin
    • 1
  • S. Lloyd
    • 2
  • D. Cella
    • 2
  1. 1.Division of Biostatistics, Department of MedicineIndiana University Medical SchoolIndianapolisUSA
  2. 2.Department of Psychology and Social SciencesRush-Presbyterian-St Luke's Medical CentreChicagoUSA

Personalised recommendations